Adaptive Budget-Constrained Execution Framework for Prioritized Regression Test Suites

In regression testing, executing all test cases is often impractical due to strict time and resource limitations. This paper presents an adaptive test execution framework designed to maximize fault detection within constrained resources. The approach formulates scheduling as a multi-objective optimization problem, aiming to increase cumulative fault detection while minimizing execution costs. A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm dynamically generates optimized execution sequences, with penalties applied to discourage infeasible solutions. An illustrative example demonstrates how the framework adapts execution plans based on available resources. Experimental evaluations on benchmark test suites show that the proposed approach consistently achieves higher fault detection efficiency compared to greedy and random scheduling strategies. This work contributes a scalable and resource-aware solution for enhancing quality assurance in real-world regression testing environments.